This year, the Australasian Data Mining Conference is excited to be running a Special Track on Statistics and Data Science. The special track will have the focus that is described below:

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Intensive-data-driven research is empowering theoretical breakthroughs and high-tech innovations, enabling new methodologies in academic discovery, and offering new sustainable means to solve significant societal and economic challenges and understand the world. Data science is a very fast growing research domain which embedded the combination of computational (i.e. computer-intensive) and inferential (i.e. statistics-oriented) thinking, with the notion of Microdata to Big Data. Since the core theories in computer science and statistical science were developed separately, there is an oil and water problem to be surmounted in data science.

For an example, the basic statistical theory does not have a place for runtime and other computational resources while core computer science theory does not have a place for statistical risk and inferential resources. As well, the most appealing challenge in Microdata is the simulation of detailed population characteristics, while in Big Data is the potential of personalised attributes. This session welcomes the full range of submissions, ranging from methodological contributions to case studies involving statistics based modellings or decisions making process (e.g., Bayesian thinking, spatial statistics, machine learning methods, modern statistics, or novel modelling techniques) in data science.

We ask that each submission include a statement about possible implications for resolving some computational and inferential challenges in data science with special focus to data mining, combining data, computation, and inferences – that is – as wide as from computing statistics or running machine learning algorithms to estimating reliability measures including standard errors and confidence intervals on their outputs in any fields.

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We invite three types of submissions for AusDM 2018:

Academic submissions: Regular academic submissions can be made in Research Track reporting on research progress, with a paper length up to 12 pages. For academic submissions we will use a double-blind review process, i.e. paper submissions must NOT include author names or affiliations (and also not acknowledgements referring to funding bodies). Self-citing references should also be removed from the submitted papers (they can be added on after the review) for the double blind reviewing purpose.

Industry submissions: Submissions can be made in the Application Track to report on specific data mining implementations and experiences in governments and industry projects. Submissions in this category can be up to 12 pages. The review process for these submissions will also be double-blind. A special committee made of industry representatives will assess industry submissions.

Industry Showcase submissions: Submission from industry and government on an analytics solution that has raised profits, reduced costs and/or achieved other important policy and/or business outcomes can be made in this track with a one page Abstract only. The review process for these submissions will also be double-blind.
Paper submissions are required to follow the general format specified for papers. LaTeX styles and Word templates will be available while LaTeX will be the recommended typesetting package.

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Further details can be found on the website: http://ausdm18.ausdm.org or by emailing h.nguyen5@latrobe.edu.au.

On behalf of the Organisation Committee and the Special Track Chairs, we look forward to seeing you at AusDM 2018!